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Record W4281389157 · doi:10.1002/bsl.2578

What do you see? Understanding perceptions of police use of force videos as a function of the camera perspective

2022· article· en· W4281389157 on OpenAlexafffund
Natasha Korva, Craig Bennell, Martin L. Lalumière, Mirza Karimullah

Bibliographic record

VenueBehavioral Sciences & the Law · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsRoyal Canadian Mounted PoliceCarleton UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPerspective (graphical)Video cameraOfficerPerceptionCamera auto-calibrationComputer scienceComputer visionArtificial intelligencePsychologyCamera resectioningLawPolitical science

Abstract

fetched live from OpenAlex

Some research suggests that video-recorded police incidents may be subject to a camera perspective bias. This study examined whether the camera angle of a recorded police use of force encounter influenced interpretation of the video. Participants (n = 330) viewed a video-recorded simulated use of force scenario in one of four camera angle conditions (body worn camera, bystander camera, security camera, all three camera angles), and then rated the conduct of the police officer and the subject. Participants' attitudes towards the police and legal system were also examined. Results indicated that camera angle did not directly impact viewers' judgment of the scenario, but pre-existing biases about the police guided their interpretations of certain camera angles. Importantly, however, this was not the case for those who viewed the body worn camera angle. These results help us understand the implications of relying on video recordings of police incidents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.183
GPT teacher head0.417
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes2
Has abstractyes

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